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Given sensor units distributed throughout an environment, we consider the problem of consolidating readings into a single coherent view when sensors wish to limit knowledge of their specific readings. Standard fusion methods make no guarantees about what curious participants may learn. For applications where privacy guarantees are required, we introduce a fusion approach that limits what can be inferred. First, it forms an aggregate stream, oblivious to the underlying sensor data, and then evaluates that stream on a combinatorial filter. This is achieved via secure multi-party computation techniques built on cryptographic primitives, which we extend and apply to the problem of fusing discrete sensor signals. We prove that the extensions preserve security under the model of semi-honest adversaries. Also, for a simple target tracking case study, we examine a proof-of-concept implementation: analyzing the (empirical) running times for components in the architecture and suggesting directions for future improvement.